scholarly journals Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations

2019 ◽  
Author(s):  
Madeline H. Kowalski ◽  
Huijun Qian ◽  
Ziyi Hou ◽  
Jonathan D. Rosen ◽  
Amanda L. Tapia ◽  
...  

AbstractMost genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are still limited. In addition to the limited inclusion of these populations in genetic studies, these populations have more complex linkage disequilibrium structure that may reduce the number of variants associated with a phenotype. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with commercial genome-wide genotyping array data. We demonstrate that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhances gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3 to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels, respectively. Impressively, even for extremely rare variants with sample minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~20,000 self-identified African descent individuals and ~23,000 self-identified Hispanic/Latino individuals identified associations with two rare variants in theHBBgene (rs33930165 with higher WBC (p=8.1×10−12) in African populations, rs11549407 with lower HGB (p=1.59×10−12) and HCT (p=1.13×10−9) in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of TOPMed imputation reference panel for identification of novel associations between rare variants and complex traits not previously detected in similar sized genome-wide studies of under-represented African and Hispanic/Latino populations.Author summaryAdmixed African and Hispanic/Latino populations remain understudied in genome-wide association and fine-mapping studies of complex diseases. These populations have more complex linkage disequilibrium (LD) structure that can impair mapping of variants associated with complex diseases and their risk factors. Genotype imputation represents an approach to improve genome coverage, especially for rare or ancestry-specific variation; however, these understudied populations also have smaller relevant imputation reference panels that need to be expanded to represent their more complex LD patterns. In this study, we leveraged >100,000 phased sequences generated from the multi-ethnic NHLBI TOPMed project to impute in admixed cohorts encompassing ~20,000 individuals of African ancestry (AAs) and ~23,000 Hispanics/Latinos. We demonstrated substantially higher imputation quality for low frequency and rare variants in comparison to the state-of-the-art reference panels (1000 Genomes Project and Haplotype Reference Consortium). Association analyses of ~35 million (AAs) and ~27 million (Hispanics/Latinos) variants passing stringent post-imputation filtering with quantitative hematological traits led to the discovery of associations with two rare variants in theHBBgene; one of these variants was replicated in an independent sample, and the other is known to cause anemia in the homozygous state. By comparison, the sameHBBvariants would not have been genome-wide significant using other state-of-the-art reference panels due to lower imputation quality. Our findings demonstrate the power of the TOPMed whole genome sequencing data for imputation and subsequent association analysis in admixed African and Hispanic/Latino populations.

2019 ◽  
Author(s):  
Mart Kals ◽  
Tiit Nikopensius ◽  
Kristi Läll ◽  
Kalle Pärn ◽  
Timo Tõnis Sikka ◽  
...  

AbstractGenotype imputation has become a standard procedure prior genome-wide association studies (GWASs). For common and low-frequency variants, genotype imputation can be performed sufficiently accurately with publicly available and ethnically heterogeneous reference datasets like 1000 Genomes Project (1000G) and Haplotype Reference Consortium panels. However, the imputation of rare variants has been shown to be significantly more accurate when ethnically matched reference panel is used. Even more, greater genetic similarity between reference panel and target samples facilitates the detection of rare (or even population-specific) causal variants. Notwithstanding, the genome-wide downstream consequences and differences of using ethnically mixed and matched reference panels have not been yet comprehensively explored.We determined and quantified these differences by performing several comparative evaluations of the discovery-driven analysis scenarios. A variant-wise GWAS was performed on seven complex diseases and body mass index by using genome-wide genotype data of ∼37,000 Estonians imputed with ethnically mixed 1000G and ethnically matched imputation reference panels. Although several previously reported common (minor allele frequency; MAF > 5%) variant associations were replicated in both resulting imputed datasets, no major differences were observed among the genome-wide significant findings or in the fine-mapping effort. In the analysis of rare (MAF < 1%) coding variants, 46 significantly associated genes were identified in the ethnically matched imputed data as compared to four genes in the 1000G panel based imputed data. All resulting genes were consequently studied in the UK Biobank data.These associations provide a solid example of how rare variants can be efficiently analysed to discover novel, potentially functional genetic variants in relevant phenotypes. Furthermore, our work serves as proof of a cost-efficient study design, demonstrating that the usage of ethnically matched imputation reference panels can enable substantially improved imputation of rare variants, facilitating novel high-confidence findings in rare variant GWAS scans.Author summaryOver the last decade, genome-wide association studies (GWASs) have been widely used for detecting genetic biomarkers in a wide range of traits. Typically, GWASs are carried out using chip-based genotyping data, which are then combined with a more densely genotyped reference panel to infer untyped genetic variants in chip-typed individuals. The latter method is called genotype imputation and its accuracy depends on multiple factors. Publicly available and ethnically heterogeneous imputation reference panels (IRPs) such as 1000 Genomes Project (1000G) are sufficiently accurate for imputation of common and low-frequency variants, but custom ethnically matched IRPs outperform these in case of rare variants. In this work, we systematically compare downstream association analysis effects on eight complex traits in ∼37,000 Estonians imputed with ethnically mixed and ethnically matched IRPs. We do not observe major differences in the single variant analysis, where both imputed datasets replicate previously reported significant loci. But in the gene-based analysis of rare protein-coding variants we show that ethnically matched panel clearly outperforms 1000G panel based imputation, providing 10-fold increase in significant gene-trait associations. Our study demonstrates empirically that imputed data based on ethnically matched panel is very promising for rare variant analysis – it captures more population-specific variants and makes it possible to efficiently identify novel findings.


2018 ◽  
Author(s):  
Degang Wu ◽  
Jinzhuang Dou ◽  
Xiaoran Chai ◽  
Claire Bellis ◽  
Andreas Wilm ◽  
...  

AbstractAsian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7×, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.


2019 ◽  
Author(s):  
Ehsan Ullah ◽  
Khalid Kunji ◽  
Ellen M. Wijsman ◽  
Mohamad Saad

AbstractMotivationImputation of untyped SNPs has become important in Genome-wide Association Studies (GWAS). There has also been a trend towards analyzing rare variants, driven by the decrease of genome sequencing costs. Rare variants are enriched in pedigrees that have many cases or extreme phenotypes. This is especially the case for large pedigrees, which makes family-based designs ideal to detect rare variants associated with complex traits. The costs of performing relatively large family-based GWAS can be significantly reduced by fully sequencing only a fraction of the pedigree and performing imputation on the remaining subjects. The program GIGI can efficiently perform imputation in large pedigrees but can be time consuming. Here, we implement GIGI’s imputation approach in a new program, GIGI2, which performs imputation with computational time reduced by at least 25x on one thread and 120x on eight threads. The memory usage of GIGI2 is reduced by at least 30x. This reduction is achieved by implementing better memory layout and a better algorithm for solving the Identity by Descent graphs, as well as with additional features, including multithreading. We also make GIGI2 available as a webserver based on the same framework as the Michigan Imputation Server.AvailabilityGIGI2 is freely available online at https://cse-git.qcri.org/eullah/GIGI2 and the websever is at https://imputation.qcri.org/[email protected]


Nature ◽  
2021 ◽  
Vol 590 (7845) ◽  
pp. 290-299 ◽  
Author(s):  
Daniel Taliun ◽  
◽  
Daniel N. Harris ◽  
Michael D. Kessler ◽  
Jedidiah Carlson ◽  
...  

AbstractThe Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.


2014 ◽  
Vol 6 (4) ◽  
pp. 846-860 ◽  
Author(s):  
Gabriel Santpere ◽  
Fleur Darre ◽  
Soledad Blanco ◽  
Antonio Alcami ◽  
Pablo Villoslada ◽  
...  

2022 ◽  
Author(s):  
Lars Wienbrandt ◽  
David Ellinghaus

Background: Reference-based phasing and genotype imputation algorithms have been developed with sublinear theoretical runtime behaviour, but runtimes are still high in practice when large genome-wide reference datasets are used. Methods: We developed EagleImp, a software with algorithmic and technical improvements and new features for accurate and accelerated phasing and imputation in a single tool. Results: We compared accuracy and runtime of EagleImp with Eagle2, PBWT and prominent imputation servers using whole-genome sequencing data from the 1000 Genomes Project, the Haplotype Reference Consortium and simulated data with more than 1 million reference genomes. EagleImp is 2 to 10 times faster (depending on the single or multiprocessor configuration selected) than Eagle2/PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical GWAS studies, EagleImp provides same or higher imputation accuracy than the Sanger Imputation Service, Michigan Imputation Server and the newly developed TOPMed Imputation Server, despite larger (not publicly available) reference panels. It has many new features, including automated chromosome splitting and memory management at runtime to avoid job aborts, fast reading and writing of large files, and various user-configurable algorithm and output options. Conclusions: Due to the technical optimisations, EagleImp can perform fast and accurate reference-based phasing and imputation for future very large reference panels with more than 1 million genomes. EagleImp is freely available for download from https://github.com/ikmb/eagleimp.


2020 ◽  
Author(s):  
Xia Shen ◽  
Ting Li ◽  
Zheng Ning

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect SNPs and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. Comparing to LDSC, the low-MAF estimator improves phenotypic correlation estimation thus is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.


Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Tanika N Kelly ◽  
Xiao Sun ◽  
Jennifer A Brody ◽  
Sarah A Gagliano ◽  
Karen Y He ◽  
...  

Background: Although genome-wide association studies (GWAS) have made important strides in localizing genomic regions associated with blood pressure (BP) phenotypes, the causal mechanisms underlying the vast majority of identified signals remain to be elucidated. Whole genome sequencing (WGS) provides an opportunity for novel genomic discoveries and high-resolution refinement of identified GWAS signals. Methods: This multi-stage genomic study of BP was conducted in an ancestrally diverse sample of up to 735,905 participants from 20 cohorts. In the discovery stage WGS study, variants with minor allele counts >10 were tested for association with systolic BP (SBP), diastolic BP (DBP), and hypertension (HTN) among 50,755 participants from the Trans-Omics for Precision Medicine and Centers for Common Disease Genomics programs using the Analysis Commons cloud based platform. Variants achieving suggestive genome-wide significance (P<1х10 -6 ) were tested for replication among UK Biobank (N=383,145) and Million Veterans Program (N=318,891) participants with GWAS data imputed to the TOPMed and 1000 Genomes reference panels, respectively. Results: Discovery stage analyses identified 63 novel loci suggestively associated with BP. As expected, most of these variants (81%) had minor allele frequencies (MAFs)<1%. Although none achieved genome-wide significance (P<5х10 -8 ) in joint analyses of discovery and replication stages, two rare variants had consistent effect directions and achieved nominal significance in replication analyses, including one for DBP at CHL1 (rs932205533; MAF=1.2х10 -4 ; joint β=18.0; joint P=7.4х10 -8 ) and one for SBP at MACROD2 (rs752530366; MAF=8.6х10 -4 ; joint β=-5.1; joint P=3.8х10 -6 ). A total of 44 novel variants from previously reported loci (r 2 <0.1 with previously reported variants) were also identified in the discovery stage analyses, including 31 rare variants with large effect sizes (70%). Nine common variants from these loci achieved genome-wide significance in joint analyses. Variants for SBP included ones at NPPB (rs12406089; MAF=0.34; joint β=-0.58; joint P=2.7х10 -79 ), AC137675.1 (rs2643826; MAF=0.56; joint β=0.56; joint P=1.5х10 -45 ), NEIL2 (rs804264; MAF=0.35; joint β=0.28; joint P=4.7х10 -20 ), CACNB2 (rs11014204; MAF=0.21; joint β=-0.53; joint P=6.8х10 -57 ), OVOL1 (rs557675; MAF=0.43; joint β=-0.25; joint P=1.9х10 -17 ), RP11-654D12.2 (rs8014582; MAF=0.05; joint β=-0.52; joint P=6.7х10 -13 ), and ATXN2 (rs35350651; MAF=0.67; joint β=-0.39; joint P=3.4х10 -38 ). Novel variants for DBP at INSR (rs36150639; MAF=0.45; joint β=-0.29; joint P=2.5х10 -27 ) and HTN at TBX3 (rs2891546; MAF=0.17; joint OR=0.95; joint P=3.1х10 -14 ) were also identified. Conclusion: WGS studies in large multi-ancestry samples can identify novel signals at previously reported GWAS loci, helping to localize causal genes and variants for BP.


2016 ◽  
Author(s):  
Julie Wertz ◽  
Qianli Liao ◽  
Thomas B Bair ◽  
Michael S Chimenti

AbstractModern genomics projects are generating millions of variant calls that must be annotated for predicted functional consequences at the level of gene expression and protein function. Many of these variants are of interest owing to their potential clinical significance. Unfortunately, state-of-the-art methods do not always agree on downstream effects for any given variant. Here we present a readily extensible python framework (PyVar) for comparing the output of variant annotator methods in order to aid the research community in quickly assessing differences between methods and benchmarking new methods as they are developed. We also apply our framework to assess the annotation performance of ANNOVAR, VEP, and SnpEff when annotating 81 million variants from the ‘1000 Genomes Project’ against both RefSeq and Ensembl human transcript sets.


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